Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations29531
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 MiB
Average record size in memory292.8 B

Variable types

Categorical2
DateTime1
Numeric12

Alerts

AQI is highly overall correlated with AQI_Bucket and 2 other fieldsHigh correlation
AQI_Bucket is highly overall correlated with AQIHigh correlation
Benzene is highly overall correlated with TolueneHigh correlation
NO is highly overall correlated with NOxHigh correlation
NO2 is highly overall correlated with NOxHigh correlation
NOx is highly overall correlated with NO and 1 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 1 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 1 other fieldsHigh correlation
Toluene is highly overall correlated with BenzeneHigh correlation
Benzene is highly skewed (γ1 = 23.63338029) Skewed
NOx has 740 (2.5%) zeros Zeros
CO has 2328 (7.9%) zeros Zeros
Benzene has 3802 (12.9%) zeros Zeros
Toluene has 2861 (9.7%) zeros Zeros

Reproduction

Analysis started2025-06-02 04:38:30.783839
Analysis finished2025-06-02 04:38:50.658073
Duration19.87 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

City
Categorical

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Ahmedabad
2009 
Bengaluru
2009 
Chennai
2009 
Mumbai
2009 
Lucknow
2009 
Other values (21)
19486 

Length

Max length18
Median length12
Mean length8.2757441
Min length5

Characters and Unicode

Total characters244391
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAhmedabad
2nd rowAhmedabad
3rd rowAhmedabad
4th rowAhmedabad
5th rowAhmedabad

Common Values

ValueCountFrequency (%)
Ahmedabad 2009
 
6.8%
Bengaluru 2009
 
6.8%
Chennai 2009
 
6.8%
Mumbai 2009
 
6.8%
Lucknow 2009
 
6.8%
Delhi 2009
 
6.8%
Hyderabad 2006
 
6.8%
Patna 1858
 
6.3%
Gurugram 1679
 
5.7%
Visakhapatnam 1462
 
5.0%
Other values (16) 10472
35.5%

Length

2025-06-02T04:38:50.758503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ahmedabad 2009
 
6.8%
bengaluru 2009
 
6.8%
chennai 2009
 
6.8%
mumbai 2009
 
6.8%
lucknow 2009
 
6.8%
delhi 2009
 
6.8%
hyderabad 2006
 
6.8%
patna 1858
 
6.3%
gurugram 1679
 
5.7%
visakhapatnam 1462
 
5.0%
Other values (16) 10472
35.5%

Most occurring characters

ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 46303
18.9%
r 21033
 
8.6%
u 15396
 
6.3%
n 15294
 
6.3%
h 13678
 
5.6%
i 13664
 
5.6%
e 11353
 
4.6%
m 10991
 
4.5%
d 8334
 
3.4%
t 8306
 
3.4%
Other values (28) 80039
32.8%

Date
Date

Distinct2009
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size230.8 KiB
Minimum2015-01-01 00:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-02T04:38:50.876132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:51.010869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation 

Distinct11716
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.510857
Minimum0.04
Maximum949.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:51.154947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile14.14
Q132.15
median48.57
Q372.45
95-th percentile180.215
Maximum949.99
Range949.95
Interquartile range (IQR)40.3

Descriptive statistics

Standard deviation59.807551
Coefficient of variation (CV)0.92709281
Kurtosis25.559898
Mean64.510857
Median Absolute Deviation (MAD)18.75
Skewness3.7383702
Sum1905070.1
Variance3576.9432
MonotonicityNot monotonic
2025-06-02T04:38:51.300198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.57 4600
 
15.6%
11 19
 
0.1%
20.75 12
 
< 0.1%
27.82 11
 
< 0.1%
29.75 10
 
< 0.1%
11.81 10
 
< 0.1%
18.81 10
 
< 0.1%
28.45 10
 
< 0.1%
47.43 10
 
< 0.1%
15 10
 
< 0.1%
Other values (11706) 24829
84.1%
ValueCountFrequency (%)
0.04 1
< 0.1%
0.16 1
< 0.1%
0.24 1
< 0.1%
0.28 1
< 0.1%
0.98 1
< 0.1%
0.99 1
< 0.1%
1.14 1
< 0.1%
1.19 1
< 0.1%
1.25 1
< 0.1%
1.39 1
< 0.1%
ValueCountFrequency (%)
949.99 1
< 0.1%
917.77 1
< 0.1%
916.67 1
< 0.1%
914.94 1
< 0.1%
914.64 1
< 0.1%
894.75 1
< 0.1%
868.66 1
< 0.1%
858.73 1
< 0.1%
832.8 1
< 0.1%
821.42 1
< 0.1%

PM10
Real number (ℝ)

High correlation 

Distinct12571
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.65937
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:51.444173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile31.605
Q179.315
median95.68
Q3111.88
95-th percentile255.335
Maximum1000
Range999.99
Interquartile range (IQR)32.565

Descriptive statistics

Standard deviation72.32402
Coefficient of variation (CV)0.65953345
Kurtosis13.209428
Mean109.65937
Median Absolute Deviation (MAD)16.27
Skewness2.8554438
Sum3238350.7
Variance5230.7639
MonotonicityNot monotonic
2025-06-02T04:38:51.577935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.68 11142
37.7%
94 9
 
< 0.1%
33.81 7
 
< 0.1%
20.53 6
 
< 0.1%
43.1 6
 
< 0.1%
109.67 6
 
< 0.1%
72.04 6
 
< 0.1%
39.46 6
 
< 0.1%
102.17 6
 
< 0.1%
84.08 6
 
< 0.1%
Other values (12561) 18331
62.1%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.02 1
< 0.1%
0.03 1
< 0.1%
0.04 2
< 0.1%
0.06 1
< 0.1%
0.07 1
< 0.1%
0.13 2
< 0.1%
0.14 2
< 0.1%
0.16 1
< 0.1%
0.17 2
< 0.1%
ValueCountFrequency (%)
1000 1
< 0.1%
985 2
< 0.1%
917.08 1
< 0.1%
847.41 1
< 0.1%
802.87 1
< 0.1%
796.88 1
< 0.1%
768.16 1
< 0.1%
763.58 1
< 0.1%
761.91 1
< 0.1%
743.98 1
< 0.1%

NO
Real number (ℝ)

High correlation 

Distinct5776
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.642601
Minimum0.02
Maximum390.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:51.711611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.88
Q16.21
median9.89
Q317.57
95-th percentile57.255
Maximum390.68
Range390.66
Interquartile range (IQR)11.36

Descriptive statistics

Standard deviation21.506064
Coefficient of variation (CV)1.2922298
Kurtosis28.90099
Mean16.642601
Median Absolute Deviation (MAD)4.67
Skewness4.1827986
Sum491472.64
Variance462.51078
MonotonicityNot monotonic
2025-06-02T04:38:51.858900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89 3592
 
12.2%
5.93 34
 
0.1%
7.78 29
 
0.1%
8.78 29
 
0.1%
0.92 28
 
0.1%
0.97 27
 
0.1%
1.94 27
 
0.1%
0.9 26
 
0.1%
2.89 26
 
0.1%
7.97 26
 
0.1%
Other values (5766) 25687
87.0%
ValueCountFrequency (%)
0.02 7
< 0.1%
0.03 3
< 0.1%
0.06 2
 
< 0.1%
0.09 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
390.68 1
< 0.1%
382.44 1
< 0.1%
351.3 1
< 0.1%
304.26 1
< 0.1%
289.75 1
< 0.1%
288.55 1
< 0.1%
287.14 1
< 0.1%
273.39 1
< 0.1%
270.09 1
< 0.1%
268.03 1
< 0.1%

NO2
Real number (ℝ)

High correlation 

Distinct7404
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.726576
Minimum0.01
Maximum362.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:51.993157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile5.4
Q112.98
median21.69
Q334.665
95-th percentile70.83
Maximum362.21
Range362.2
Interquartile range (IQR)21.685

Descriptive statistics

Standard deviation23.050531
Coefficient of variation (CV)0.83135152
Kurtosis13.252875
Mean27.726576
Median Absolute Deviation (MAD)10.04
Skewness2.697408
Sum818793.51
Variance531.32698
MonotonicityNot monotonic
2025-06-02T04:38:52.121953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.69 3590
 
12.2%
10.58 24
 
0.1%
9.42 23
 
0.1%
9.14 18
 
0.1%
10.21 17
 
0.1%
7.14 17
 
0.1%
9.44 17
 
0.1%
9.47 17
 
0.1%
9.24 17
 
0.1%
10.09 17
 
0.1%
Other values (7394) 25774
87.3%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 5
< 0.1%
0.03 9
< 0.1%
0.04 2
 
< 0.1%
0.05 3
 
< 0.1%
0.06 3
 
< 0.1%
0.07 7
< 0.1%
0.08 5
< 0.1%
0.09 7
< 0.1%
0.1 4
< 0.1%
ValueCountFrequency (%)
362.21 1
< 0.1%
292.02 1
< 0.1%
277.31 1
< 0.1%
273.39 1
< 0.1%
266.46 1
< 0.1%
245.62 1
< 0.1%
241.34 1
< 0.1%
239.18 1
< 0.1%
239.1 1
< 0.1%
237.27 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Zeros 

Distinct8156
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.063568
Minimum0
Maximum467.63
Zeros740
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:52.771204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.41
Q114.67
median23.52
Q336.015
95-th percentile90.13
Maximum467.63
Range467.63
Interquartile range (IQR)21.345

Descriptive statistics

Standard deviation29.477748
Coefficient of variation (CV)0.94894919
Kurtosis13.246348
Mean31.063568
Median Absolute Deviation (MAD)10.14
Skewness2.8521721
Sum917338.24
Variance868.93764
MonotonicityNot monotonic
2025-06-02T04:38:52.904034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.52 4193
 
14.2%
0 740
 
2.5%
4.22 208
 
0.7%
6.24 115
 
0.4%
4.3 35
 
0.1%
2.21 31
 
0.1%
4.95 19
 
0.1%
4.14 18
 
0.1%
4.47 17
 
0.1%
4.97 16
 
0.1%
Other values (8146) 24139
81.7%
ValueCountFrequency (%)
0 740
2.5%
0.03 4
 
< 0.1%
0.04 9
 
< 0.1%
0.05 3
 
< 0.1%
0.06 2
 
< 0.1%
0.07 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
467.63 1
< 0.1%
382.84 1
< 0.1%
378.31 1
< 0.1%
378.24 1
< 0.1%
302.78 1
< 0.1%
293.1 1
< 0.1%
289.09 1
< 0.1%
287.89 1
< 0.1%
273.33 1
< 0.1%
271.94 1
< 0.1%

NH3
Real number (ℝ)

Distinct5922
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.813789
Minimum0.01
Maximum352.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:53.025121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.66
Q112.04
median15.85
Q321.755
95-th percentile53.64
Maximum352.89
Range352.88
Interquartile range (IQR)9.715

Descriptive statistics

Standard deviation21.028862
Coefficient of variation (CV)1.0103332
Kurtosis44.355841
Mean20.813789
Median Absolute Deviation (MAD)4.37
Skewness5.2046589
Sum614651.99
Variance442.21305
MonotonicityNot monotonic
2025-06-02T04:38:53.161707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.85 10332
35.0%
6.29 36
 
0.1%
6.32 29
 
0.1%
6.31 28
 
0.1%
6.3 28
 
0.1%
6.28 27
 
0.1%
6.27 24
 
0.1%
10.46 23
 
0.1%
6.59 22
 
0.1%
6.6 21
 
0.1%
Other values (5912) 18961
64.2%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.02 6
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.08 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 4
< 0.1%
0.12 3
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
352.89 1
< 0.1%
328.89 1
< 0.1%
323.48 1
< 0.1%
309.04 1
< 0.1%
303.53 1
< 0.1%
302.08 1
< 0.1%
301.28 1
< 0.1%
301.18 1
< 0.1%
297.64 1
< 0.1%
296.43 1
< 0.1%

CO
Real number (ℝ)

Zeros 

Distinct1779
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1538722
Minimum0
Maximum175.81
Zeros2328
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:53.290762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.54
median0.89
Q31.38
95-th percentile7.155
Maximum175.81
Range175.81
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation6.7246605
Coefficient of variation (CV)3.122126
Kurtosis117.79595
Mean2.1538722
Median Absolute Deviation (MAD)0.4
Skewness9.2101442
Sum63606
Variance45.221058
MonotonicityNot monotonic
2025-06-02T04:38:53.434747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2328
 
7.9%
0.89 2262
 
7.7%
0.68 209
 
0.7%
0.85 208
 
0.7%
0.8 205
 
0.7%
0.84 200
 
0.7%
0.78 200
 
0.7%
0.81 199
 
0.7%
0.64 198
 
0.7%
0.67 194
 
0.7%
Other values (1769) 23328
79.0%
ValueCountFrequency (%)
0 2328
7.9%
0.01 59
 
0.2%
0.02 59
 
0.2%
0.03 56
 
0.2%
0.04 30
 
0.1%
0.05 48
 
0.2%
0.06 42
 
0.1%
0.07 40
 
0.1%
0.08 34
 
0.1%
0.09 38
 
0.1%
ValueCountFrequency (%)
175.81 1
< 0.1%
145.32 1
< 0.1%
134.85 1
< 0.1%
132.47 1
< 0.1%
132.07 1
< 0.1%
124.01 1
< 0.1%
119.68 1
< 0.1%
119.3 1
< 0.1%
118.02 1
< 0.1%
118 1
< 0.1%

SO2
Real number (ℝ)

Distinct4761
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.830897
Minimum0.01
Maximum193.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:53.585166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.8
Q16.09
median9.16
Q313.81
95-th percentile43.165
Maximum193.86
Range193.85
Interquartile range (IQR)7.72

Descriptive statistics

Standard deviation17.005647
Coefficient of variation (CV)1.2295404
Kurtosis25.900578
Mean13.830897
Median Absolute Deviation (MAD)3.51
Skewness4.4248517
Sum408440.22
Variance289.19203
MonotonicityNot monotonic
2025-06-02T04:38:53.728841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.16 3864
 
13.1%
5.74 36
 
0.1%
6.12 35
 
0.1%
4.65 32
 
0.1%
5.81 32
 
0.1%
5.53 32
 
0.1%
6.61 32
 
0.1%
5.95 31
 
0.1%
5.57 31
 
0.1%
6.47 31
 
0.1%
Other values (4751) 25375
85.9%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.04 1
< 0.1%
0.21 1
< 0.1%
0.26 1
< 0.1%
0.36 1
< 0.1%
0.41 2
< 0.1%
0.42 1
< 0.1%
0.44 1
< 0.1%
0.48 1
< 0.1%
0.49 1
< 0.1%
ValueCountFrequency (%)
193.86 1
< 0.1%
187.02 1
< 0.1%
186.08 1
< 0.1%
182.39 1
< 0.1%
180.85 1
< 0.1%
179.18 1
< 0.1%
178.93 1
< 0.1%
178.63 1
< 0.1%
178.58 1
< 0.1%
176.88 1
< 0.1%

O3
Real number (ℝ)

Distinct7699
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.994121
Minimum0.01
Maximum257.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:53.866251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile7.67
Q120.74
median30.84
Q342.73
95-th percentile71.78
Maximum257.73
Range257.72
Interquartile range (IQR)21.99

Descriptive statistics

Standard deviation20.202304
Coefficient of variation (CV)0.59428817
Kurtosis4.5298247
Mean33.994121
Median Absolute Deviation (MAD)10.92
Skewness1.4959537
Sum1003880.4
Variance408.13307
MonotonicityNot monotonic
2025-06-02T04:38:54.002747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.84 4030
 
13.6%
16.48 17
 
0.1%
22.14 15
 
0.1%
23.6 15
 
0.1%
18.33 14
 
< 0.1%
19.64 14
 
< 0.1%
13.14 13
 
< 0.1%
22.94 13
 
< 0.1%
19.68 13
 
< 0.1%
32.06 13
 
< 0.1%
Other values (7689) 25374
85.9%
ValueCountFrequency (%)
0.01 4
< 0.1%
0.02 7
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 3
 
< 0.1%
0.07 1
 
< 0.1%
0.1 8
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
257.73 1
< 0.1%
200.41 1
< 0.1%
193.31 1
< 0.1%
186.07 1
< 0.1%
177.07 1
< 0.1%
175.04 1
< 0.1%
172.28 1
< 0.1%
169.36 1
< 0.1%
169.35 1
< 0.1%
165.48 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1873
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.859874
Minimum0
Maximum455.03
Zeros3802
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:54.137749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.24
median1.07
Q32.42
95-th percentile8.23
Maximum455.03
Range455.03
Interquartile range (IQR)2.18

Descriptive statistics

Standard deviation14.252822
Coefficient of variation (CV)4.9837235
Kurtosis653.45478
Mean2.859874
Median Absolute Deviation (MAD)0.93
Skewness23.63338
Sum84454.94
Variance203.14292
MonotonicityNot monotonic
2025-06-02T04:38:54.266540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07 5667
 
19.2%
0 3802
 
12.9%
0.03 300
 
1.0%
0.02 292
 
1.0%
0.01 217
 
0.7%
0.04 190
 
0.6%
0.05 176
 
0.6%
2 170
 
0.6%
0.09 170
 
0.6%
0.1 167
 
0.6%
Other values (1863) 18380
62.2%
ValueCountFrequency (%)
0 3802
12.9%
0.01 217
 
0.7%
0.02 292
 
1.0%
0.03 300
 
1.0%
0.04 190
 
0.6%
0.05 176
 
0.6%
0.06 146
 
0.5%
0.07 123
 
0.4%
0.08 157
 
0.5%
0.09 170
 
0.6%
ValueCountFrequency (%)
455.03 1
< 0.1%
454.85 1
< 0.1%
449.38 1
< 0.1%
448.59 1
< 0.1%
445.83 1
< 0.1%
443.63 1
< 0.1%
438.01 1
< 0.1%
435.9 1
< 0.1%
435.09 1
< 0.1%
432.94 1
< 0.1%

Toluene
Real number (ℝ)

High correlation  Zeros 

Distinct3608
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1404849
Minimum0
Maximum454.85
Zeros2861
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:54.404312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.28
median2.97
Q36.02
95-th percentile31.46
Maximum454.85
Range454.85
Interquartile range (IQR)4.74

Descriptive statistics

Standard deviation17.224737
Coefficient of variation (CV)2.4122644
Kurtosis290.69125
Mean7.1404849
Median Absolute Deviation (MAD)2.03
Skewness13.490402
Sum210865.66
Variance296.69157
MonotonicityNot monotonic
2025-06-02T04:38:54.554304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.97 8058
27.3%
0 2861
 
9.7%
0.02 111
 
0.4%
0.03 102
 
0.3%
0.05 99
 
0.3%
0.04 86
 
0.3%
1.1 83
 
0.3%
6 79
 
0.3%
0.08 76
 
0.3%
0.06 72
 
0.2%
Other values (3598) 17904
60.6%
ValueCountFrequency (%)
0 2861
9.7%
0.01 70
 
0.2%
0.02 111
 
0.4%
0.03 102
 
0.3%
0.04 86
 
0.3%
0.05 99
 
0.3%
0.06 72
 
0.2%
0.07 61
 
0.2%
0.08 76
 
0.3%
0.09 54
 
0.2%
ValueCountFrequency (%)
454.85 1
< 0.1%
454.12 1
< 0.1%
449.14 1
< 0.1%
448.87 1
< 0.1%
445.84 1
< 0.1%
443.63 1
< 0.1%
437.77 1
< 0.1%
435.94 1
< 0.1%
434.92 1
< 0.1%
433.02 1
< 0.1%

AQI
Real number (ℝ)

High correlation 

Distinct829
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.78155
Minimum13
Maximum2049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size230.8 KiB
2025-06-02T04:38:54.687822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile52
Q188
median118
Q3179
95-th percentile391
Maximum2049
Range2036
Interquartile range (IQR)91

Descriptive statistics

Standard deviation130.27241
Coefficient of variation (CV)0.82045056
Kurtosis25.833384
Mean158.78155
Median Absolute Deviation (MAD)38
Skewness3.7697515
Sum4688978
Variance16970.902
MonotonicityNot monotonic
2025-06-02T04:38:54.845567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 4829
 
16.4%
102 223
 
0.8%
100 222
 
0.8%
70 208
 
0.7%
106 208
 
0.7%
78 198
 
0.7%
98 195
 
0.7%
104 192
 
0.7%
66 192
 
0.7%
80 190
 
0.6%
Other values (819) 22874
77.5%
ValueCountFrequency (%)
13 1
 
< 0.1%
14 3
 
< 0.1%
15 3
 
< 0.1%
16 4
 
< 0.1%
17 7
 
< 0.1%
18 2
 
< 0.1%
19 27
0.1%
20 29
0.1%
21 7
 
< 0.1%
22 8
 
< 0.1%
ValueCountFrequency (%)
2049 1
< 0.1%
1917 1
< 0.1%
1842 1
< 0.1%
1747 1
< 0.1%
1719 1
< 0.1%
1672 1
< 0.1%
1646 1
< 0.1%
1630 1
< 0.1%
1613 1
< 0.1%
1595 1
< 0.1%

AQI_Bucket
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Moderate
13510 
Satisfactory
8224 
Poor
2781 
Very Poor
2337 
Good
 
1341

Length

Max length12
Median length9
Mean length8.5441401
Min length4

Characters and Unicode

Total characters252317
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 13510
45.7%
Satisfactory 8224
27.8%
Poor 2781
 
9.4%
Very Poor 2337
 
7.9%
Good 1341
 
4.5%
Severe 1338
 
4.5%

Length

2025-06-02T04:38:54.980554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-02T04:38:55.082461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate 13510
42.4%
satisfactory 8224
25.8%
poor 5118
 
16.1%
very 2337
 
7.3%
good 1341
 
4.2%
severe 1338
 
4.2%

Most occurring characters

ValueCountFrequency (%)
o 34652
13.7%
e 33371
13.2%
r 30527
12.1%
a 29958
11.9%
t 29958
11.9%
d 14851
 
5.9%
M 13510
 
5.4%
y 10561
 
4.2%
S 9562
 
3.8%
i 8224
 
3.3%
Other values (8) 37143
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 34652
13.7%
e 33371
13.2%
r 30527
12.1%
a 29958
11.9%
t 29958
11.9%
d 14851
 
5.9%
M 13510
 
5.4%
y 10561
 
4.2%
S 9562
 
3.8%
i 8224
 
3.3%
Other values (8) 37143
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 34652
13.7%
e 33371
13.2%
r 30527
12.1%
a 29958
11.9%
t 29958
11.9%
d 14851
 
5.9%
M 13510
 
5.4%
y 10561
 
4.2%
S 9562
 
3.8%
i 8224
 
3.3%
Other values (8) 37143
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 34652
13.7%
e 33371
13.2%
r 30527
12.1%
a 29958
11.9%
t 29958
11.9%
d 14851
 
5.9%
M 13510
 
5.4%
y 10561
 
4.2%
S 9562
 
3.8%
i 8224
 
3.3%
Other values (8) 37143
14.7%

Interactions

2025-06-02T04:38:48.918992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:32.811829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.585495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.897766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.223793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.854683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.056031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.381583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.669294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.400872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.032574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.713438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.025930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:32.943700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.760271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.008600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.322739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.952822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.162249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.481392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.775993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.497590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.180750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.809305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.135599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.082443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.853006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.113253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.426157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.049300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.263124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.580774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.884436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.596618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.320521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.901986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.251681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.241466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.959327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.231601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.536983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.154374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.374648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.697905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.008505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.705840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.475678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.007176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.389571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.384806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.060436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.342194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.648664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.262737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.483549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.804132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.126077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.846783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.638361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.109117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.493818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.512921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.159601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.442540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.749282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.351704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.586004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.904491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.229556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:44.992052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.781742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.198988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.608027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.671712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.262314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.552315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.870272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.452415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.699913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.029931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.338022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.153794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:46.928175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.315485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.725176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.815351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.372294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.667825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.317324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.551373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.812249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.137905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.448669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.301111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.095159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.424539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.842627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:33.976485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.480474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.792321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.428033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.659296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:40.931587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.252140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.561141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.463845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.282642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.528076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:49.948817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.119676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.574905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.896230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.528543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.753362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.039490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.356986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.665936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.602714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.421771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.626553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:50.055547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.263808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.677721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:36.995940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.635342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.845603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.144732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.453637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.773714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.742918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.510010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.722047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:50.165803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:34.419067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:35.793639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:37.098503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:38.733162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:39.952510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:41.264329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:42.554547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:43.875000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:45.878985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:47.604578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-02T04:38:48.814511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-02T04:38:55.182106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AQIAQI_BucketBenzeneCOCityNH3NONO2NOxO3PM10PM2.5SO2Toluene
AQI1.0000.5780.2150.4650.2080.2750.4280.4240.4230.2670.6750.8130.3490.283
AQI_Bucket0.5781.0000.0230.2600.3500.0910.1820.2350.1970.1440.3170.4200.1950.123
Benzene0.2150.0231.0000.2420.0990.0890.2180.2800.2370.1370.2100.1900.1480.710
CO0.4650.2600.2421.0000.1740.1660.3170.2430.3060.0640.2290.3280.2230.315
City0.2080.3500.0990.1741.0000.2160.1360.1770.1710.1630.2200.1630.2180.129
NH30.2750.0910.0890.1660.2161.0000.2730.3890.2310.1690.2930.2940.0670.069
NO0.4280.1820.2180.3170.1360.2731.0000.4660.702-0.0570.3960.3970.3300.184
NO20.4240.2350.2800.2430.1770.3890.4661.0000.5880.2950.4050.4270.2260.324
NOx0.4230.1970.2370.3060.1710.2310.7020.5881.0000.0370.4010.3920.3160.257
O30.2670.1440.1370.0640.1630.169-0.0570.2950.0371.0000.2260.2600.1870.195
PM100.6750.3170.2100.2290.2200.2930.3960.4050.4010.2261.0000.6840.3020.235
PM2.50.8130.4200.1900.3280.1630.2940.3970.4270.3920.2600.6841.0000.2620.226
SO20.3490.1950.1480.2230.2180.0670.3300.2260.3160.1870.3020.2621.0000.239
Toluene0.2830.1230.7100.3150.1290.0690.1840.3240.2570.1950.2350.2260.2391.000

Missing values

2025-06-02T04:38:50.364742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-02T04:38:50.529838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneAQIAQI_Bucket
0Ahmedabad1/1/201548.5795.680.9218.2217.1515.850.9227.64133.360.000.02118.0Moderate
1Ahmedabad1/2/201548.5795.680.9715.6916.4615.850.9724.5534.063.685.50118.0Moderate
2Ahmedabad1/3/201548.5795.6817.4019.3029.7015.8517.4029.0730.706.8016.40118.0Moderate
3Ahmedabad1/4/201548.5795.681.7018.4817.9715.851.7018.5936.084.4310.14118.0Moderate
4Ahmedabad1/5/201548.5795.6822.1021.4237.7615.8522.1039.3339.317.0118.89118.0Moderate
5Ahmedabad1/6/201548.5795.6845.4138.4881.5015.8545.4145.7646.515.4210.83118.0Moderate
6Ahmedabad1/7/201548.5795.68112.1640.62130.7715.85112.1632.2833.470.000.00118.0Moderate
7Ahmedabad1/8/201548.5795.6880.8736.7496.7515.8580.8738.5431.890.000.00118.0Moderate
8Ahmedabad1/9/201548.5795.6829.1631.0048.0015.8529.1658.6825.750.000.00118.0Moderate
9Ahmedabad1/10/201548.5795.689.897.040.0015.850.898.294.550.000.00118.0Moderate
CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneAQIAQI_Bucket
29521Visakhapatnam6/22/202033.17108.225.5842.4527.0613.700.7313.6534.853.9910.2495.0Satisfactory
29522Visakhapatnam6/23/202025.4083.382.7634.0919.9213.130.5410.4043.272.8812.03100.0Satisfactory
29523Visakhapatnam6/24/202034.3690.901.2223.3813.1214.450.5610.9235.122.993.1586.0Satisfactory
29524Visakhapatnam6/25/202013.4558.542.3021.6013.0912.270.418.1929.381.285.6477.0Satisfactory
29525Visakhapatnam6/26/20207.6332.275.9123.2717.1911.150.466.8719.901.455.3747.0Good
29526Visakhapatnam6/27/202015.0250.947.6825.0619.5412.470.478.5523.302.2412.0741.0Good
29527Visakhapatnam6/28/202024.3874.093.4226.0616.5311.990.5212.7230.140.742.2170.0Satisfactory
29528Visakhapatnam6/29/202022.9165.733.4529.5318.3310.710.488.4230.960.010.0168.0Satisfactory
29529Visakhapatnam6/30/202016.6449.974.0529.2618.8010.030.529.8428.300.000.0054.0Satisfactory
29530Visakhapatnam7/1/202015.0066.000.4026.8514.055.200.592.1017.051.072.9750.0Good